Citation
When using this resource, please cite the original publication:
Aysan Mahmoudzadeh, Iman Azimi, Amir M. Rahmani, and Pasi Liljeberg, “Lightweight Photoplethysmography Quality Assessment for Real-time IoT-based Health Monitoring using Unsupervised Anomaly Detection,” Elsevier International Conference on Ambient Systems, Networks and Technologies (ANT’21), 2021, Poland.
Abstract
The proposed PPG quality assessment method includes three parts: 1) filtering, 2) feature extraction, and 3) clustering. The method, enabled by an elliptical envelope, requires low computational resources. It differentiates valid and noisy PPG signals collected from wearable devices. More details can be found in the paper.